Gastos Casa
21 noviembre, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 2/10/2022 | Electricidad | 48087 | Andrés | atrasado del mes anterior |
| 4/10/2022 | Comida | 41760 | Andrés | NA |
| 4/10/2022 | Comida | 12860 | Andrés | NA |
| 8/10/2022 | Brussels | 25300 | Tami | NA |
| 8/10/2022 | Comida | 25300 | Tami | NA |
| 10/10/2022 | Comida | 67895 | Tami | NA |
| 11/10/2022 | Enceres | 11730 | Andrés | Ida easy |
| 11/10/2022 | Enceres | 7146 | Andrés | Uber easy |
| 15/10/2022 | Tres toques | 28600 | Tami | NA |
| 17/10/2022 | Comida | 47140 | Andrés | NA |
| 19/10/2022 | Comida | 28110 | Andrés | FREST verduras y frutas |
| 23/10/2022 | Comida | 76701 | Tami | NA |
| 26/10/2022 | Comida | 35941 | Tami | NA |
| 26/10/2022 | Enceres | 11980 | Andrés | Mascarilla |
| 27/10/2022 | Comida | 17536 | Tami | NA |
| 30/10/2022 | VTR | 21990 | Andrés | entel |
| 28/10/2022 | Comida | 27940 | Andrés | tres toques |
| 3/11/2022 | Diosi | 56000 | Tami | Vacunas |
| 4/11/2022 | Electricidad | 49266 | Andrés | Pac enel |
| 6/11/2022 | Comida | 19325 | Tami | NA |
| 8/11/2022 | Agua | 10092 | Andrés | NA |
| 9/11/2022 | Diosi | 117980 | Andrés | 58990 por 2 |
| 9/11/2022 | Comida | 73462 | Tami | NA |
| 9/11/2022 | Diosi | 17535 | Tami | Correa petsu |
| 12/11/2022 | Gas | 76350 | Andrés | NA |
| 12/11/2022 | Enceres | 16986 | Andrés | uber ida matri fran |
| 14/11/2022 | Comida | 51263 | Tami | NA |
| 19/11/2022 | Comida | 2943 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.7672e+08 2 5.2455 0.0056 **
## lag_depvar 7.7922e+10 1 1714.7877 <2e-16 ***
## Residuals 2.3402e+10 515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 969.7489 13487.93 0.0187695
## 2-0 27332.951 21577.5892 33088.31 0.0000000
## 2-1 20104.113 16635.7831 23572.44 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 363 49567.21 15757.551
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2043.228037 4050.802689 -548.390892 2429.125898 -2990.037024
## 7 8 9 10 11
## 511.329294 -5666.753174 -1175.385446 -3952.417835 -391.212960
## 12 13 14 15 16
## -4916.765882 -1570.403284 -860.980603 413.016048 -3215.618298
## 17 18 19 20 21
## -342.383076 -2099.631114 6637.698849 -1530.531166 -1205.157418
## 22 23 24 25 26
## 1481.289224 -1190.221050 234.333899 1691.499192 -7114.584408
## 27 28 29 30 31
## 964.929930 8201.421098 388.916023 -43.470406 -2428.380484
## 32 33 34 35 36
## 1560.094525 4549.574182 1085.201431 2348.088147 -1918.206491
## 37 38 39 40 41
## 4569.982544 4281.377580 -2313.563186 -3004.914159 -1118.041780
## 42 43 44 45 46
## -10743.762786 7333.199813 2564.111208 1361.684362 8094.614714
## 47 48 49 50 51
## 642.015618 6487.087742 6649.833228 -5967.689323 -4844.953186
## 52 53 54 55 56
## -5082.585708 -7926.998751 6165.020476 -4072.316914 -4873.275169
## 57 58 59 60 61
## 3895.001250 905.340420 -20.825494 152.032298 -4988.666682
## 62 63 64 65 66
## 18154.808685 3587.226109 -3708.564578 5886.306782 7284.407816
## 67 68 69 70 71
## 14555.221683 1557.296626 -13338.756935 -1359.573710 4602.382422
## 72 73 74 75 76
## -4956.229828 -4432.430764 -10502.928101 2507.319882 -5374.972039
## 77 78 79 80 81
## 1108.716533 -6831.589830 606.830583 -2304.613991 -2640.075465
## 82 83 84 85 86
## -3873.266667 -469.340516 2376.463598 3806.598737 498.814877
## 87 88 89 90 91
## -467.682359 213.227660 4315.397075 -1170.063895 1149.527186
## 92 93 94 95 96
## -2070.789957 -1041.061895 184.751481 280.102827 -7480.755828
## 97 98 99 100 101
## 2426.952220 -8582.203254 -2885.505405 -3978.488698 -1665.782297
## 102 103 104 105 106
## -1191.684403 3247.373501 -2297.894032 2642.695801 -1127.265911
## 107 108 109 110 111
## 1002.580568 2610.805721 -3145.065541 -4701.331514 -810.848893
## 112 113 114 115 116
## 1941.568876 11718.403592 -1271.854169 2648.214510 4233.375790
## 117 118 119 120 121
## 3458.316318 -1154.002936 -4758.865732 -3740.837438 2321.263207
## 122 123 124 125 126
## -1741.495745 1340.151755 8852.106319 803.172153 88.300423
## 127 128 129 130 131
## -2558.744846 2633.187673 7021.784004 954.859430 -8554.212022
## 132 133 134 135 136
## 1738.114482 4118.108243 -3197.248529 -1435.201154 -861.440574
## 137 138 139 140 141
## -3882.927568 1197.252780 -488.310754 -2905.301654 1737.968198
## 142 143 144 145 146
## -1871.365912 -7812.765077 2087.818175 -3446.464515 2146.245620
## 147 148 149 150 151
## -228.344511 1049.405169 -340.899877 1369.640137 1195.801616
## 152 153 154 155 156
## 3359.237233 -4874.247735 -1164.381287 -3222.045564 5982.660976
## 157 158 159 160 161
## 9743.233286 -3139.304301 -4477.213113 3918.518821 484.348524
## 162 163 164 165 166
## 2978.681135 -5645.419441 -6459.573411 4468.273473 17676.161084
## 167 168 169 170 171
## 3820.799964 -218.353663 -2257.850528 -899.335258 3803.524988
## 172 173 174 175 176
## -30.361061 -7872.771358 3107.659571 4553.641134 832.826987
## 177 178 179 180 181
## 8957.017942 -9081.468274 -3253.263347 -10508.746440 -10954.378179
## 182 183 184 185 186
## 1566.623733 9605.690227 -1173.448550 6187.673310 6780.141305
## 187 188 189 190 191
## 13348.626254 8556.555589 -3971.847027 2587.938108 10487.062180
## 192 193 194 195 196
## -1569.588029 -2348.518850 -10160.735120 -6182.064757 1449.511027
## 197 198 199 200 201
## -5025.105907 -9561.295610 5665.508399 -2822.046583 -1454.584672
## 202 203 204 205 206
## -543.083063 6753.353157 10099.449042 739.652149 3086.416045
## 207 208 209 210 211
## 3248.001448 5923.389222 12948.917918 -5630.210247 -11191.828395
## 212 213 214 215 216
## -5490.426844 -10377.705137 -4809.442107 1813.061446 -12741.056597
## 217 218 219 220 221
## 16721.047036 8038.890865 1712.176978 26865.693355 12560.710110
## 222 223 224 225 226
## 7319.582438 13995.860526 -3993.971617 -1767.616250 3786.823811
## 227 228 229 230 231
## 372.223858 2779.944025 9045.413380 5845.300087 -1895.975442
## 232 233 234 235 236
## -1782.146671 9501.843464 -11465.985112 -7159.848636 -8368.924869
## 237 238 239 240 241
## -9879.834422 3351.103422 1601.526752 -8059.627006 -8711.158730
## 242 243 244 245 246
## 9412.948701 -7509.085243 2779.441196 -10032.586815 -3737.375636
## 247 248 249 250 251
## 1747.825617 1305.969262 -12030.425525 3985.564031 2367.977972
## 252 253 254 255 256
## 4493.219776 2382.956223 -930.450800 11371.342809 21045.885136
## 257 258 259 260 261
## 3245.341154 -4226.170655 4194.480988 -1621.254068 3833.174045
## 262 263 264 265 266
## -4766.253766 -10768.820834 -4534.850884 -301.689700 -4968.908931
## 267 268 269 270 271
## 9023.037540 -4093.488231 4399.804942 -1924.644576 4624.645424
## 272 273 274 275 276
## 874.839190 7465.557724 -1291.528761 12159.482274 -4518.568608
## 277 278 279 280 281
## 1828.886898 -272.102439 7961.474384 -4988.336591 -2619.412722
## 282 283 284 285 286
## -11124.989261 -2457.859640 18879.938175 7887.867204 2797.519750
## 287 288 289 290 291
## -570.224253 980.996392 6478.411564 6932.630394 -18751.805835
## 292 293 294 295 296
## -10979.224330 -7885.130122 9950.353784 3285.893755 -987.204200
## 297 298 299 300 301
## 27601.632999 10084.383974 4872.110657 9480.953654 2782.182224
## 302 303 304 305 306
## -1095.948364 7868.414721 -24350.583703 -3397.840258 -5.932466
## 307 308 309 310 311
## -6792.599432 -3744.640619 3185.401900 -8962.553008 -2938.559686
## 312 313 314 315 316
## -7879.524064 1919.743158 -2821.793586 2387.157805 -3771.690961
## 317 318 319 320 321
## 27772.879062 -619.995537 3408.733837 10932.344829 5625.117416
## 322 323 324 325 326
## 32394.974976 4922.466457 -21114.684748 1805.808631 1133.839016
## 327 328 329 330 331
## -6428.006712 -1631.777008 -33140.045004 1290.581976 -1913.194476
## 332 333 334 335 336
## 300.685236 -2785.999968 4478.340728 -87.664672 -6609.203137
## 337 338 339 340 341
## -2731.105483 -1796.398317 -7282.301402 4290.610334 -980.463586
## 342 343 344 345 346
## -1351.498150 -608.733480 555.287643 845.688306 -1270.166054
## 347 348 349 350 351
## -9097.092168 -12802.084783 2797.955821 -3878.669949 -3202.974360
## 352 353 354 355 356
## -5519.442843 2232.793242 1827.014156 3162.693432 -3397.541708
## 357 358 359 360 361
## -134.298368 1046.783018 7362.787693 558.911904 236.284056
## 362 363 364 365 366
## 2853.271292 -2503.716121 -609.750434 -8470.801468 -4286.285734
## 367 368 369 370 371
## -5844.733039 -4544.432273 -6824.281067 5481.538646 773.264062
## 372 373 374 375 376
## 7501.269062 -7326.785127 -1899.533601 -3018.219083 -2082.677863
## 377 378 379 380 381
## -12067.279124 2376.859374 -10199.610766 6194.457477 9760.681929
## 382 383 384 385 386
## 3454.750137 -2106.050591 1909.674325 7028.065234 11633.711486
## 387 388 389 390 391
## -5673.164536 -5181.261010 69.111050 8789.696291 1970.358883
## 392 393 394 395 396
## 11367.793401 -9818.012978 2927.478211 848.757747 700.111527
## 397 398 399 400 401
## -513.170514 -409.804674 -14323.125509 8816.684678 -958.554655
## 402 403 404 405 406
## -1137.129469 7230.097563 -7742.123737 -1033.808708 -2256.896649
## 407 408 409 410 411
## -5523.310271 -2516.296912 -3557.259853 -8371.761685 6577.852054
## 412 413 414 415 416
## 2013.468306 -7027.332019 -7294.356272 14668.066363 4119.253138
## 417 418 419 420 421
## 4752.284353 -7818.899734 -4457.248731 -2276.832400 3159.905402
## 422 423 424 425 426
## -13703.096361 -2370.968618 -8671.170891 3499.545849 7413.428418
## 427 428 429 430 431
## 6931.779757 -3699.675763 -3803.728453 -4376.910132 -1413.824417
## 432 433 434 435 436
## -5333.564157 -6213.454811 -5497.836002 -912.953094 -380.242565
## 437 438 439 440 441
## -4523.695905 3052.962398 5265.043264 -4691.186866 -1762.924459
## 442 443 444 445 446
## 1974.349809 -3467.989357 3224.274929 -6227.644320 -11715.525660
## 447 448 449 450 451
## -4031.176118 10138.800932 -1642.841105 5146.735630 -5530.103156
## 452 453 454 455 456
## -742.834185 760.798032 3388.276965 -11941.485614 3789.324781
## 457 458 459 460 461
## -6325.087186 6940.913612 3361.463774 2822.772790 -3555.675262
## 462 463 464 465 466
## 2411.830193 289.355118 2086.753518 -244.688041 3630.373286
## 467 468 469 470 471
## -2387.561455 6080.892488 -6713.068858 -2672.268467 -1889.101421
## 472 473 474 475 476
## -4332.182544 3361.362897 8129.320689 -5753.587364 1798.862637
## 477 478 479 480 481
## -5879.117433 -2496.043592 2376.693560 -12589.841663 -9318.640316
## 482 483 484 485 486
## -706.052352 501.850895 -503.407315 -895.564922 -9146.547204
## 487 488 489 490 491
## 11590.951498 6618.846840 7742.620104 -5178.166283 5671.257929
## 492 493 494 495 496
## 9551.583021 6240.721559 -13324.609244 -10294.486830 -3083.391583
## 497 498 499 500 501
## -727.895901 -147.096875 -7254.020716 1034.767351 4692.261191
## 502 503 504 505 506
## 5866.933045 967.627340 380.765146 -6940.383472 925.073347
## 507 508 509 510 511
## -4704.436329 2210.604112 -942.169575 -7800.232131 -187.843109
## 512 513 514 515 516
## -2269.206782 -174.268610 1736.530926 -9114.058740 -7320.387719
## 517 518 519 520
## 24773.971108 10100.001129 6077.936137 -5173.930427
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17226.06 20088.20 24364.53 24081.02 26446.75 23765.38 24485.47 19692.53
## 10 11 12 13 14 15 16 17
## 19427.70 16756.50 17538.05 14250.26 14301.69 14969.84 16675.33 14986.53
## 18 19 20 21 22 23 24 25
## 16026.63 15396.87 22516.53 21595.73 21072.85 22972.79 22295.24 22951.22
## 26 27 28 29 30 31 32 33
## 24806.87 18703.36 20438.58 28317.08 28375.04 28046.24 25663.19 27073.00
## 34 35 36 37 38 39 40 41
## 30936.23 31286.48 32703.06 30200.59 34161.62 37386.56 34427.20 31221.33
## 42 43 44 45 46 47 48 49
## 30063.05 20593.09 28151.32 30600.60 31695.53 38569.56 38061.48 42748.17
## 50 51 52 53 54 55 56 57
## 47006.69 39666.24 34206.16 29202.71 22311.12 28634.17 25196.85 21475.00
## 58 59 60 61 62 63 64 65
## 25906.52 27172.68 27471.25 27885.24 23734.48 40412.92 42266.56 37487.55
## 66 67 68 69 70 71 72 73
## 41716.59 46658.06 57382.27 55385.61 40551.29 38044.05 41077.80 35348.00
## 74 75 76 77 78 79 80 81
## 30776.36 21430.97 24649.26 20553.57 22650.59 17519.31 19545.33 18767.79
## 82 83 84 85 86 87 88 89
## 17790.41 15849.20 17133.68 20760.69 25201.61 26196.68 26221.77 26841.75
## 90 91 92 93 94 95 96 97
## 30988.49 29812.90 30817.50 28871.78 28067.39 28437.47 28846.18 22389.90
## 98 99 100 101 102 103 104 105
## 25420.77 18414.65 17264.77 15295.21 15596.54 16277.48 20773.61 19852.30
## 106 107 108 109 110 111 112 113
## 23381.84 23170.71 24855.62 27747.49 25232.47 21657.28 21934.15 24594.31
## 114 115 116 117 118 119 120 121
## 35515.85 33699.21 35546.34 38560.40 40526.57 38202.87 32996.69 29318.88
## 122 123 124 125 126 127 128 129
## 31412.64 29683.56 30871.32 38510.97 38151.56 37208.17 34055.24 35845.79
## 130 131 132 133 134 135 136 137
## 41272.00 40709.35 31864.89 33136.32 36342.82 32734.63 31113.44 30193.64
## 138 139 140 141 142 143 144 145
## 26732.60 28154.45 27922.87 25597.03 27632.08 26249.62 19818.18 22864.61
## 146 147 148 149 150 151 152 153
## 20679.90 23672.63 24215.45 25814.19 25997.22 27660.06 28967.62 32015.68
## 154 155 156 157 158 159 160 161
## 27462.10 26721.19 24263.62 30188.62 41159.73 39481.21 36832.34 41878.94
## 162 163 164 165 166 167 168 169
## 43294.89 46728.71 42170.86 37453.44 42907.12 59294.77 61518.50 59924.28
## 170 171 172 173 174 175 176 177
## 56733.34 55124.19 57840.93 56859.91 49111.63 51949.93 55712.17 55748.55
## 178 179 180 181 182 183 184 185
## 62914.75 53367.26 50101.18 40861.66 32356.66 35883.31 46039.73 45492.90
## 186 187 188 189 190 191 192 193
## 51476.86 57251.95 68091.44 73401.99 67063.63 67258.08 74365.45 70019.23
## 194 195 196 197 198 199 200 201
## 65518.59 54706.06 48704.92 50136.68 45708.30 37836.06 44294.48 42512.58
## 202 203 204 205 206 207 208 209
## 42148.65 42629.50 49459.12 58394.92 58022.58 59756.43 61420.90 65231.94
## 210 211 212 213 214 215 216 217
## 74748.07 66789.40 54916.57 49497.13 40446.30 37388.08 40518.06 30485.95
## 218 219 220 221 222 223 224 225
## 47548.39 54907.54 55814.16 78698.86 86233.13 88246.85 95877.97 86781.47
## 226 227 228 229 230 231 232 233
## 80748.46 80328.20 76960.63 76117.73 80879.56 82250.98 76657.29 71845.16
## 234 235 236 237 238 239 240 241
## 77528.41 64106.28 56101.07 48009.55 39577.18 43791.04 45955.06 39371.44
## 242 243 244 245 246 247 248 249
## 33017.91 43354.23 37570.99 41527.30 33750.66 32449.75 36124.17 38962.85
## 250 251 252 253 254 255 256 257
## 29744.29 35713.45 39534.78 44756.76 47489.31 46979.23 57334.11 74922.94
## 258 259 260 261 262 263 264 265
## 74737.03 68012.66 69502.25 65703.25 67156.97 60881.96 50100.42 46106.98
## 266 267 268 269 270 271 272 273
## 46317.48 42403.82 51254.06 47507.62 51676.07 49782.78 53871.45 54169.01
## 274 275 276 277 278 279 280 281
## 60217.96 57839.80 67563.43 61456.40 61667.53 60007.95 65780.91 59478.56
## 282 283 284 285 286 287 288 289
## 56024.42 45522.00 43910.35 61232.85 66791.91 67203.51 64607.58 63690.16
## 290 291 292 293 294 295 296 297
## 67712.08 71642.81 52539.80 42589.99 36569.65 46945.11 50203.92 49313.22
## 298 299 300 301 302 303 304 305
## 73636.33 79612.89 80284.05 84920.67 83109.81 78114.01 81599.01 56366.27
## 306 307 308 309 310 311 312 313
## 52607.79 52285.89 46043.50 43238.31 46860.55 39373.70 38089.10 32622.11
## 314 315 316 317 318 319 320 321
## 36426.51 35603.56 39455.12 37428.98 63350.57 61180.41 62812.51 70852.60
## 322 323 324 325 326 327 328 329
## 73252.45 98867.82 97236.97 72940.33 71731.88 70080.58 61990.06 59097.19
## 330 331 332 333 334 335 336 337
## 28887.85 32594.77 33036.60 35368.71 34706.09 40503.38 41584.63 36807.25
## 338 339 340 341 342 343 344 345
## 36017.54 36144.87 31439.25 37469.75 38136.64 38396.45 39276.86 41072.17
## 346 347 348 349 350 351 352 353
## 42903.74 42654.09 35561.66 26079.90 31452.67 30307.69 29895.59 27499.49
## 354 355 356 357 358 359 360 361
## 32202.99 35977.02 40464.11 38643.58 39910.50 42060.21 49494.37 50047.86
## 362 363 364 365 366 367 368 369
## 50250.59 52726.72 50196.89 49638.52 42245.00 39427.02 35583.86 33350.85
## 370 371 372 373 374 375 376 377
## 29387.89 36714.16 39013.16 46940.21 40880.11 40324.36 38853.96 38384.28
## 378 379 380 381 382 383 384 385
## 29203.85 33826.18 26841.26 35103.89 45491.39 49075.62 47339.90 49342.08
## 386 387 388 389 390 391 392 393
## 55595.00 65130.45 58305.98 52745.03 52472.30 59890.78 60416.92 69131.30
## 394 395 396 397 398 399 400 401
## 58179.52 59754.67 59312.46 58793.60 57272.52 56027.55 42716.32 51347.27
## 402 403 404 405 406 407 408 409
## 50342.42 49303.19 55738.27 48241.38 47548.90 45866.74 41521.15 40345.69
## 410 411 412 413 414 415 416 417
## 38399.33 32462.29 40376.67 43318.47 37962.64 33024.93 47975.18 51840.29
## 418 419 420 421 422 423 424 425
## 55790.33 48219.68 44523.55 43192.52 46797.95 35155.83 34883.60 29112.03
## 426 427 428 429 430 431 432 433
## 34731.43 43103.08 50031.68 46780.01 43833.20 40742.11 40629.71 37088.88
## 434 435 436 437 438 439 440 441
## 33206.84 30426.24 32010.67 33869.84 31863.89 36755.81 42994.19 39729.35
## 442 443 444 445 446 447 448 449
## 39433.79 42456.13 40331.01 44341.64 39563.38 30548.18 29379.48 40796.56
## 450 451 452 453 454 455 456 457
## 40476.41 46157.53 41770.55 42122.06 43751.15 47489.06 37309.68 42184.66
## 458 459 460 461 462 463 464 465
## 37583.66 45192.82 48731.51 51365.96 48078.17 50431.36 50633.96 52390.26
## 466 467 468 469 470 471 472 473
## 51885.20 54844.56 52158.68 57236.64 50460.84 48059.10 46637.75 43244.21
## 474 475 476 477 478 479 480 481
## 47020.25 54523.16 48920.57 50632.83 45394.04 43764.45 46612.41 35970.50
## 482 483 484 485 486 487 488 489
## 29497.91 31377.15 34088.12 35585.99 36556.98 30164.05 42760.72 49456.24
## 490 491 492 493 494 495 496 497
## 56322.74 51006.17 55864.85 63538.99 67370.61 53554.06 44081.96 42096.47
## 498 499 500 501 502 503 504 505
## 42421.38 43216.74 37674.23 40085.88 45415.50 51127.23 51840.66 51951.81
## 506 507 508 509 510 511 512 513
## 45620.36 46967.44 43206.82 45976.88 45640.80 39323.27 40460.35 39631.13
## 514 515 516 517 518 519 520
## 40742.61 43396.63 36198.82 31453.17 55469.43 63673.35 67345.64
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8476
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.245511 0.5881362 3.112268
## t2* 1714.787732 28.8909735 246.831846
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.589925 5.385518 11.48167
## 2 lag_depvar 1365.705161 1728.010464 2172.25665Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Nov 21 00:55:35 2022
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 5.4832 | 5.682818 | 7.184559 |
| Comida | NA | 309.9132 | 314.267773 | 342.032206 |
| Comunicaciones | NA | 0.0000 | 0.000000 | 0.000000 |
| Electricidad | NA | 43.8829 | 36.050227 | 30.598824 |
| Enceres | NA | 22.4051 | 18.257500 | 25.582618 |
| Farmacia | NA | 2.1980 | 8.633318 | 10.540412 |
| Gas/Bencina | NA | 45.5550 | 28.116091 | 24.283941 |
| Diosi | NA | 14.5163 | 35.337136 | 35.966853 |
| donaciones/regalos | NA | 0.0000 | 7.821909 | 8.079971 |
| Electrodomésticos/ Mantención casa | NA | 4.7328 | 33.021273 | 24.396118 |
| VTR | NA | 25.7900 | 22.133773 | 21.067882 |
| Netflix | NA | 6.9259 | 6.982000 | 7.428559 |
| Otros | NA | 3.7813 | 1.718773 | 1.112147 |
| Total | 0 | 485.1837 | 518.022591 | 538.274088 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1804, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-12-09 00:04:58 sería de: 35.506 pesos// Percentil 95% más alto proyectado: 38.720,43
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 34884.62 | 34876.56 |
| Lo.80 | 34911.29 | 34897.34 |
| Point.Forecast | 35505.54 | 36589.34 |
| Hi.80 | 37282.91 | 41179.92 |
| Hi.95 | 38259.54 | 43610.02 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3170 990.6885
## s.e. 0.1473 35.2789
##
## sigma^2 = 27645: log likelihood = -292.99
## AIC=591.99 AICc=592.57 BIC=597.41paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.3143 874.785 3.8838
## s.e. 0.1479 516.143 17.2520
##
## sigma^2 = 28272: log likelihood = -292.97
## AIC=593.94 AICc=594.94 BIC=601.16forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 676.2442 | 647.0860 | 684.8307 |
| Lo.80 | 796.4056 | 766.0188 | 765.4038 |
| Point.Forecast | 1023.3958 | 990.6883 | 944.3703 |
| Hi.80 | 1250.3860 | 1215.3577 | 1236.6942 |
| Hi.95 | 1370.5474 | 1334.2906 | 1426.4585 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Andrés | Tami |
|---|---|---|---|
| 1 | marzo_2019 | 68268 | 175533 |
| 2 | abril_2019 | 55031 | 152640 |
| 3 | mayo_2019 | 192219 | 152985 |
| 4 | junio_2019 | 84961 | 291067 |
| 5 | julio_2019 | 205893 | 241389 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.4
## [7] tidytext_0.3.4 DT_0.26 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.12.2
## [13] forecast_8.18 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.0 tm_0.7-9 NLP_0.2-1
## [19] tsibble_1.1.3 forcats_0.5.2 dplyr_1.0.10
## [22] purrr_0.3.5 tidyr_1.2.1 tibble_3.1.8
## [25] ggplot2_3.4.0 tidyverse_1.3.2 sjPlot_2.8.12
## [28] lattice_0.20-45 gridExtra_2.3 plotrix_3.8-2
## [31] sparklyr_1.7.8 httr_1.4.4 readxl_1.4.1
## [34] zoo_1.8-11 stringr_1.4.1 stringi_1.7.8
## [37] DataExplorer_0.8.2 data.table_1.14.6 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.3
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.2.0 lme4_1.1-31
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] its.analysis_1.6.0 withr_2.5.0 colorspace_2.0-3
## [10] ggfortify_0.4.15 highr_0.9 knitr_1.41
## [13] uuid_1.1-0 rstudioapi_0.14 TTR_0.24.3
## [16] labeling_0.4.2 emmeans_1.8.2 slam_0.1-50
## [19] bit64_4.0.5 farver_2.1.1 datawizard_0.6.4
## [22] fBasics_4021.93 rprojroot_2.0.3 vctrs_0.5.0
## [25] generics_0.1.3 xfun_0.35 timechange_0.1.1
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.6.0
## [34] nnet_7.3-16 googlesheets4_1.0.1 gtable_0.3.1
## [37] spatial_7.3-14 timeDate_4021.106 rlang_1.0.6
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.1 selectr_0.4-2
## [46] broom_1.0.1 yaml_2.3.6 abind_1.4-5
## [49] modelr_0.1.10 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.3 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 jquerylib_0.1.4
## [58] Rcpp_1.0.9 base64enc_0.1-3 fracdiff_1.5-2
## [61] haven_2.5.1 fs_1.5.2 magrittr_2.0.3
## [64] timeSeries_4021.105 lmtest_0.9-40 reprex_2.0.2
## [67] googledrive_2.0.0 mvtnorm_1.1-3 sjmisc_2.8.9
## [70] hms_1.1.2 evaluate_0.18 xtable_1.8-4
## [73] sjstats_0.18.2 ggeffects_1.1.4 compiler_4.1.2
## [76] KernSmooth_2.23-20 crayon_1.5.2 minqa_1.2.5
## [79] htmltools_0.5.3 tzdb_0.3.0 lubridate_1.9.0
## [82] DBI_1.1.3 sjlabelled_1.2.0 dbplyr_2.2.1
## [85] MASS_7.3-54 boot_1.3-28 Matrix_1.5-3
## [88] car_3.1-1 cli_3.4.1 quadprog_1.5-8
## [91] parallel_4.1.2 insight_0.18.7 igraph_1.3.5
## [94] pkgconfig_2.0.3 xml2_1.3.3 bslib_0.4.1
## [97] estimability_1.4.1 anytime_0.3.9 snakecase_0.11.0
## [100] janeaustenr_1.0.0 digest_0.6.30 janitor_2.1.0
## [103] rmarkdown_2.18 cellranger_1.1.0 curl_4.3.3
## [106] gtools_3.9.3 urca_1.3-3 nloptr_2.0.3
## [109] lifecycle_1.0.3 nlme_3.1-153 jsonlite_1.8.3
## [112] tseries_0.10-52 carData_3.0-5 viridisLite_0.4.1
## [115] fansi_1.0.3 pillar_1.8.1 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.13.0 bit_4.0.5
## [121] sass_0.4.2 performance_0.10.0 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))